Introduction

library(oneiric)
library(scater)
library(ggplot2)
library(RColorBrewer)
set.seed(288)

Data

Chaos Parameters

data(oneiric)
str(map_params)
##  num [1:36, 1:6] -1.452 -0.983 1.536 -0.91 2.613 ...
##  - attr(*, "dimnames")=List of 2
##   ..$ : NULL
##   ..$ : chr [1:6] "a" "b" "c" "d" ...

Simulated Territories

sim_ter <- paste0(system.file(package = "oneiric"), "/inst/extdata/")
dir(sim_ter)
## character(0)

Preparing output directory

if (!dir.exists("output/")) {
    output <- dir.create("output/")
}
output <- "output/"

Creating New Data sets

Simulate New Territories

circle <- simulate_spatial(n_cells = 6000,
    n_territories = 5,
    n_samples = 12,
    pattern = "circle",
    max_expanse = 0.3)

rod <- simulate_spatial(n_cells = 6000,
    n_territories = 5,
    n_samples = 12,
    pattern = "rod",
    max_width = 0.1,
    max_length = 0.5)

chaos_map <- simulate_spatial(n_cells = 6000,
    n_samples = 12,
    pattern = "chaos",
    max_expanse = 0.02)

Circular Territories

circles <- do.call("rbind", circle)
circles$Territory <- as.factor(circles$Territory)
g <- ggplot(circles, aes(x = x, y = y, col = Territory)) +
    geom_point(size = 0.5) +
    theme_bw() +
    scale_color_brewer(palette = "Spectral") +
    facet_wrap(~sample) +
    guides(colour = guide_legend(
        override.aes = list(size =  5)))
print(g)
Circular Territories

Circular Territories

Rod Territories

rods <- do.call("rbind", rod)
rods$Territory <- as.factor(rods$Territory)
g <- ggplot(rods, aes(x = x, y = y, col = Territory)) +
    geom_point(size = 0.5) +
    theme_bw() +
    scale_color_brewer(palette = "Spectral") +
    facet_wrap(~sample) +
    guides(colour = guide_legend(
        override.aes = list(size =  5)))
print(g)
Rod Territories

Rod Territories

Tinkerbell Map Territories

chaos <- do.call("rbind", chaos_map)
chaos$Territory <- as.factor(chaos$Territory)
g <- ggplot(chaos, aes(x = x, y = y, col = Territory)) +
    geom_point(size = 0.5) +
    theme_bw() +
    scale_color_brewer(palette = "Spectral") +
    facet_wrap(~sample) +
    guides(colour = guide_legend(
        override.aes = list(size =  5)))
print(g)
Tinkerbell Map Territories

Tinkerbell Map Territories

Simulate New Cells

circle_counts <- simulate_cells(circle,
    cell_composition = 2,
    n_genes = 2000,
    seed = 1729)
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
rod_counts <- simulate_cells(rod,
    cell_composition = 2,
    n_genes = 2000,
    seed = 1729)
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
chaos_counts <- simulate_cells(chaos_map,
    cell_composition = 2,
    n_genes = 2000,
    seed = 1729)
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!
## Getting parameters...
## Creating simulation object...
## Simulating library sizes...
## Simulating gene means...
## Simulating group DE...
## Simulating cell means...
## Simulating BCV...
## Simulating counts...
## Simulating dropout (if needed)...
## Sparsifying assays...
## Automatically converting to sparse matrices, threshold = 0.95
## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'BCV': estimated sparse size 1.5 * dense matrix
## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix
## Skipping 'counts': estimated sparse size 2.4 * dense matrix
## Done!

Export Data sets

export_simulation(spatial = circle,
    cells = circle_counts,
    out_dir = output,
    file_tag = "circle_spatial_territories")
export_simulation(spatial = rod,
    cells = rod_counts,
    out_dir = output,
    file_tag = "circle_spatial_territories")
export_simulation(spatial = choas_map,
    cells = chaos_counts,
    out_dir = output,
    file_tag = "circle_spatial_territories")